Diverse Series Research Articles

Quantifying the global film festival circuit: Networks, diversity, and public value creation.

Film festivals are a key component in the global film industry in terms of trendsetting, publicity, trade, and collaboration. We present an unprecedented analysis of the international film festival circuit, which has so far remained relatively understudied quantitatively, partly due to the limited availability of suitable data sets. We use large-scale data from the Cinando platform of the Cannes Film Market, widely used by industry professionals. We explicitly model festival events as a global network connected by shared films and quantify festivals as aggregates of the metadata of their showcased films. Importantly, we argue against using simple count distributions for discrete labels such as language or production country, as such categories are typically not equidistant. Rather, we propose embedding them in continuous latent vector spaces. We demonstrate how these "festival embeddings" provide insight into changes in programmed content over time, predict festival connections, and can be used to measure diversity in film festival programming across various cultural, social, and geographical variables-which all constitute an aspect of public value creation by film festivals. Our results provide a novel mapping of the film festival circuit between 2009-2021 (616 festivals, 31,989 unique films), highlighting festival types that occupy specific niches, diverse series, and those that evolve over time. We also discuss how these quantitative findings fit into media studies and research on public value creation by cultural industries. With festivals occupying a central position in the film industry, investigations into the data they generate hold opportunities for researchers to better understand industry dynamics and cultural impact, and for organizers, policymakers, and industry actors to make more informed, data-driven decisions. We hope our proposed methodological approach to festival data paves way for more comprehensive film festival studies and large-scale quantitative cultural event analytics in general.

A novel damped conformable fractional grey Bernoulli model and its applications in energy prediction with uncertainties

Energy resources, such as oil, coal are important to people's life and the development of the economy. Predicting the energy consumption and production with uncertainties can help the government and policymakers make the reasonable energy strategy and give constructive guidance. In this paper, a novel damped conformable fractional accumulation operator with two parameters are proposed to have control of both accumulation generation and new information involvement. The new accumulation can be adjusted from linear approximation to high order accumulation. A structure adaptive grey Bernoulli model based on proposed accumulation method is established, with the parameters optimized by particle swarm optimization algorithm. For testifying the effectiveness and the robustness of the model, two validation cases and two application cases are used to build the prediction model of diverse series with unexpected trends or fluctuations in energy fields. Compared with other existing fractional grey models and non-grey models, the proposed model has best prediction accuracy, better model stability and greatly improved applicability. The global coal consumption by industry is predicted as 30.85×106 TJ in 2025 and 30.81×106 TJ in 2026. The world coal production could be 182.93–187.73 exajoules in 2025 and 180.85–182.16 exajoules in 2026.

Chemical and Antiplasmodial Investigations on Eremophila-Derived Alkaloids and Semisynthetic Ether Analogues.

Microthecaline A (1), the known antiplasmodial quinoline serrulatane alkaloid from the roots of Eremophila microtheca F. Muell. ex Benth. (Scrophulariaceae), was targeted for isolation and subsequent use in the generation of a semisynthetic ether library. A large-scale extraction and isolation yielded the previously undescribed quinoline serrulatane microthecaline B (2), along with crystalline 1 that enabled the first X-ray crystallographic analysis to be undertaken on this rare alkaloid structure class. The X-ray diffraction analysis of 1 supported the absolute configuration assignment of microthecaline A, which was originally assigned by ECD data analysis. Microthecaline A (1) was converted into 10 new semisynthetic ether derivatives (3-12) using a diverse series of commercially available alkyl halides. Chemical structures of the new serrulatane alkaloid and semisynthetic ether analogues were assigned by spectroscopic and spectrometric analyses. Antiplasmodial evaluations of 1-12 showed that the semisynthetic derivative 5 elicited the most potent activity with an IC50 value of 7.2 μM against Plasmodium falciparum 3D7 (drug-sensitive) strain.

Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects.

Medicinal chemistry and drug design efforts can be assisted by machine learning (ML) models that relate the molecular structure to compound properties. Such quantitative structure-property relationship models are generally trained on large data sets that include diverse chemical series (global models). In the pharmaceutical industry, these ML global models are available across discovery projects as an "out-of-the-box" solution to assist in drug design, synthesis prioritization, and experiment selection. However, drug discovery projects typically focus on confined parts of the chemical space (e.g., chemical series), where global models might not be applicable. Local ML models are sometimes generated to focus on specific projects or series. Herein, ML-based global models, local models, and hybrid global-local strategies were benchmarked. Analyses were done for more than 300 drug discovery projects at Novartis and ten absorption, distribution, metabolism, and excretion (ADME) assays. In this work, hybrid global-local strategies based on transfer learning approaches were proposed to leverage both historical ADME data (global) and project-specific data (local) to adapt model predictions. Fine-tuning a pretrained global ML model (used for weights' initialization, WI) was the top-performing method. Average improvements of mean absolute errors across all assays were 16% and 27% compared with global and local models, respectively. Interestingly, when the effect of training set size was analyzed, WI fine-tuning was found to be successful even in low-data scenarios (e.g., ∼10 molecules per project). Taken together, this work highlights the potential of domain adaptation in the field of molecular property predictions to refine existing pretrained models on a new compound data distribution.

Powering Electricity Forecasting with Transfer Learning

Accurate forecasting is one of the keys to the efficient use of the limited existing energy resources and plays an important role in sustainable development. While most of the current research has focused on energy price forecasting, very few studies have considered medium-term (monthly) electricity generation. This research aims to fill this gap by proposing a novel forecasting approach based on zero-shot transfer learning. Specifically, we train a Neural Basis Expansion Analysis for Time Series (NBEATS) model on a vast dataset comprising diverse time series data. Then, the trained model is applied to forecast electric power generation using zero-shot learning. The results show that the proposed method achieves a lower error than the benchmark deep learning and statistical methods, especially in backtesting. Furthermore, the proposed method provides vastly superior execution time as it does not require problem-specific training.

Improved synthesis and evaluation of preclinical pharmacodynamic parameters of a new monocyclic β-lactam DPI-2016

Monocyclic β-lactams are stable to a number of β-lactamases and are the focus of researchers for the development of antibacterial drugs, particularly against Enterobacterales. We recently synthesized and reported the bactericidal activity of diverse series of aztreonam appended with amidine moieties as siderophores. One of the derivatives exhibiting the highest MIC value in vitro was selected for further preclinical studies. The compound DPI-2016 was reassessed for its synthetic routes and methods that were improved to find the maximum final yields aimed at large-scale synthesis. In addition, the results of the pharmacological studies were determined with reference to aztreonam. It has been found that the compound DPI-2016 showed comparable or slightly improved ADMET as well as pharmacokinetic parameters to aztreonam. It is estimated that the compound could be a potential lead for further clinical evaluation.

MTS-LOF: Medical Time-Series Representation Learning via Occlusion-Invariant Features.

Medical time series data are indispensable in healthcare, providing critical insights for disease diagnosis, treatment planning, and patient management. The exponential growth in data complexity, driven by advanced sensor technologies, has presented challenges related to data labeling. Self-supervised learning (SSL) has emerged as a transformative approach to address these challenges, eliminating the need for extensive human annotation. In this study, we introduce a novel framework for Medical Time Series Representation Learning, known as MTS-LOF. MTS-LOF leverages the strengths of Joint-Embedding SSL and Masked Autoencoder (MAE) methods, offering a unique approach to representation learning for medical time series data. By combining these techniques, MTS-LOF enhances the potential of healthcare applications by providing more sophisticated, context-rich representations. Additionally, MTS-LOF employs a multi-masking strategy to facilitate occlusion-invariant feature learning. This approach allows the model to create multiple views of the data by masking portions of it. By minimizing the discrepancy between the representations of these masked patches and the fully visible patches, MTS-LOF learns to capture rich contextual information within medical time series datasets. The results of experiments conducted on diverse medical time series datasets demonstrate the superiority of MTS-LOF over other methods. These findings hold promise for significantly enhancing healthcare applications by improving representation learning. Furthermore, our work delves into the integration of Joint-Embedding SSL and MAE techniques, shedding light on the intricate interplay between temporal and structural dependencies in healthcare data. This understanding is crucial, as it allows us to grasp the complexities of healthcare data analysis.

MTEA Diversity, Equity, and Inclusion Committee Focus Group Series: Addressing Anti-Semitism Inclusive Musical Theatre Pedagogy

As an ongoing effort to support inclusive musical theater training within our musical theater educational community, the MTEA Diversity, Equity, and ELIANA RUBIN, Inclusion Committee hosts a Focus Group Series. On July 29th, 2023, Vice IONAH SCHWARTZ., AND President of the MTEA DEI Committee Gwen Walker convened a focus group BECCA SUSKAUER to discuss rising anti-Semitism and what musical theatre educators can do to address it. Included in our discussion from our community is an established professional, an educator, a young professional, and a student currently in an academic musical theater program.

Traffic Transformer: Transformer-based framework for temporal traffic accident prediction

<abstract><p>Reliable prediction of traffic accidents is crucial for the identification of potential hazards in advance, formulation of effective preventative measures, and reduction of accident incidence. Existing neural network-based models generally suffer from a limited field of perception and poor long-term dependency capturing abilities, which severely restrict their performance. To address the inherent shortcomings of current traffic prediction models, we propose the Traffic Transformer for multidimensional, multi-step traffic accident prediction. Initially, raw datasets chronicling sporadic traffic accidents are transformed into multivariate, regularly sampled sequences that are amenable to sequential modeling through a temporal discretization process. Subsequently, Traffic Transformer captures and learns the hidden relationships between any elements of the input sequence, constructing accurate prediction for multiple forthcoming intervals of traffic accidents. Our proposed Traffic Transformer employs the sophisticated multi-head attention mechanism in lieu of the widely used recurrent architecture. This significant shift enhances the model's ability to capture long-range dependencies within time series data. Moreover, it facilitates a more flexible and comprehensive learning of diverse hidden patterns within the sequences. It also offers the versatility of convenient extension and transference to other diverse time series forecasting tasks, demonstrating robust potential for further development in this field. Extensive comparative experiments conducted on a real-world dataset from Qatar demonstrate that our proposed Traffic Transformer model significantly outperforms existing mainstream time series forecasting models across all evaluation metrics and forecast horizons. Notably, its Mean Absolute Percentage Error reaches a minimal value of only 4.43%, which is substantially lower than the error rates observed in other models. This remarkable performance underscores the Traffic Transformer's state-of-the-art level of in predictive accuracy.</p></abstract>

Thirty Years in the Design and Development of Cruzain Inhibitors

Cruzipain is the principal protease of Trypanosoma cruzi, the etiological agent of Chagas disease. Since its discovery in the 1980s and the resolution of the crystal structure of cruzain (a truncated recombinant form of the enzyme) in 1995, this target has attracted the interest of many research groups for screening studies, structure-based and ligand-based drug design campaigns, which include peptide-like and non-peptide synthetic compounds. In this context, empirical and computational methods have proven to be valuable tools for the study of mechanisms of action, potential binding modes and structure-activity relationships for a diverse series of cruzain/cruzipain inhibitors. This paper, therefore, reviews some of the most relevant chemical groups reported as cruzain inhibitors over the last 30 years of research.

ADAPTING TO CYBERSECURITY CHALLENGES: ASSESSING THE EFFECTIVENESS OF INTERNATIONAL LAW AGAINST CYBER TERRORISM

The study critically examines current condition of global rule in resolving the developing hazard of cyber terrorism. The research dives right into the complexities and problems posed by cyber terrorism as well as reviews the effectiveness of existing lawful platforms in responding to this progressing threat. Employing a diverse series of academic jobs, the study investigates the requirements for brand-new global lawful frameworks, developments in cybercrime inspection as well as digital forensics, as well as the function of surfacing modern technologies in rule administration. Also, the report stresses the significance of enhancing global cyber resilience and response abilities by means of worldwide teamwork and collaboration. The study's searching highlights varied attributes of cyber violence and demand for a complete, collaborated, and international reaction to fight this threat. The analysis uses valuable knowledge right into advancement of brand-new lawful platforms, technologies in cybercrime examination & enlargement of global cyber resilience to deal with problems positioned through cyber terrorism.

The Carolina hysterectomy cohort (CHC): a novel case series of reproductive-aged hysterectomy patients across 10 hospitals in the US south

BackgroundHysterectomy is a common surgery among reproductive-aged U.S. patients, with rates highest among Black patients in the South. There is limited insight on causes of these racial differences. In the U.S., electronic medical records (EMR) data can offer richer detail on factors driving surgical decision-making among reproductive-aged populations than insurance claims-based data. Our objective in this cohort profile paper is to describe the Carolina Hysterectomy Cohort (CHC), a large EMR-based case-series of premenopausal hysterectomy patients in the U.S. South, supplemented with census and surgeon licensing data. To demonstrate one strength of the data, we evaluate whether patient and surgeon characteristics differ by insurance payor type.MethodsWe used structured and abstracted EMR data to identify and characterize patients aged 18–44 years who received hysterectomies for non-cancerous conditions between 10/02/2014–12/31/2017 in a large health care system comprised of 10 hospitals in North Carolina. We used Chi-squared and Kruskal Wallis tests to compare whether patients’ socio-demographic and relevant clinical characteristics, and surgeon characteristics differed by patient insurance payor (public, private, uninsured).ResultsOf 1857 patients (including 55% non-Hispanic White, 30% non-Hispanic Black, 9% Hispanic), 75% were privately-insured, 17% were publicly-insured, and 7% were uninsured. Menorrhagia was more prevalent among the publicly-insured (74% vs 68% overall). Fibroids were more prevalent among the privately-insured (62%) and the uninsured (68%). Most privately insured patients were treated at non-academic hospitals (65%) whereas most publicly insured and uninsured patients were treated at academic centers (66 and 86%, respectively). Publicly insured and uninsured patients had higher median bleeding (public: 7.0, uninsured: 9.0, private: 5.0) and pain (public: 6.0, uninsured: 6.0, private: 3.0) symptom scores than the privately insured. There were no statistical differences in surgeon characteristics by payor groups.ConclusionThis novel study design, a large EMR-based case series of hysterectomies linked to physician licensing data and manually abstracted data from unstructured clinical notes, enabled identification and characterization of a diverse reproductive-aged patient population more comprehensively than claims data would allow. In subsequent phases of this research, the CHC will leverage these rich clinical data to investigate multilevel drivers of hysterectomy in an ethnoracially, economically, and clinically diverse series of hysterectomy patients.

Junior scientists spotlight social bonds in seminars for diversity, equity, and inclusion in STEM.

Disparities for women and minorities in science, technology, engineering, and math (STEM) careers have continued even amidst mounting evidence for the superior performance of diverse workforces. In response, we launched the Diversity and Science Lecture series, a cross-institutional platform where junior life scientists present their research and comment on diversity, equity, and inclusion in STEM. We characterize speaker representation from 79 profiles and investigate topic noteworthiness via quantitative content analysis of talk transcripts. Nearly every speaker discussed interpersonal support, and three-fifths of speakers commented on race or ethnicity. Other topics, such as sexual and gender minority identity, were less frequently addressed but highly salient to the speakers who mentioned them. We found that significantly co-occurring topics reflected not only conceptual similarity, such as terms for racial identities, but also intersectional significance, such as identifying as a Latina/Hispanic woman or Asian immigrant, and interactions between concerns and identities, including the heightened value of friendship to the LGBTQ community, which we reproduce using transcripts from an independent seminar series. Our approach to scholar profiles and talk transcripts serves as an example for transmuting hundreds of hours of scholarly discourse into rich datasets that can power computational audits of speaker diversity and illuminate speakers' personal and professional priorities.

Characterization of Structurally Diverse 18F-Labeled d-TCO Derivatives as a PET Probe for Bioorthogonal Pretargeted Imaging.

Background: The pretargeted imaging strategy using inverse electron demand Diels-Alder (IEDDA) cycloaddition between a trans-cyclooctene (TCO) and tetrazine (Tz) has emerged and rapidly grown as a promising concept to improve radionuclide imaging and therapy in oncology. This strategy has mostly relied on the use of radiolabeled Tz together with TCO-modified targeting vectors leading to a rapid growth of the number of available radiolabeled tetrazines, while only a few radiolabeled TCOs are currently reported. Here, we aim to develop novel and structurally diverse 18F-labeled cis-dioxolane-fused TCO (d-TCO) derivatives to further expand the bioorthogonal toolbox for in vivo ligation and evaluate their potential for positron emission tomography (PET) pretargeted imaging. Results: A small series of d-TCO derivatives were synthesized and tested for their reactivity against tetrazines, with all compounds showing fast reaction kinetics with tetrazines. A fluorescence-based pretargeted blocking study was developed to investigate the in vivo ligation of these compounds without labor-intensive prior radiochemical development. Two compounds showed excellent in vivo ligation results with blocking efficiencies of 95 and 97%. Two novel 18F-labeled d-TCO radiotracers were developed, from which [18F]MICA-214 showed good in vitro stability, favorable pharmacokinetics, and moderate in vivo stability. Micro-PET pretargeted imaging with [18F]MICA-214 in mice bearing LS174T tumors treated with tetrazine-modified CC49 monoclonal antibody (mAb) (CC49-Tz) showed significantly higher uptake in tumor tissue in the pretargeted group (CC49-Tz 2.16 ± 0.08% ID/mL) when compared to the control group with nonmodified mAb (CC49 1.34 ± 0.07% ID/mL). Conclusions: A diverse series of fast-reacting fluorinated d-TCOs were synthesized. A pretargeted blocking approach in tumor-bearing mice allowed the choice of a lead compound with fast reaction kinetics with Tz. A novel 18F-labeled d-TCO tracer was developed and used in a pretargeted PET imaging approach, allowing specific tumor visualization in a mouse model of colorectal cancer. Although further optimization of the radiotracer is needed to enhance the tumor-to-background ratios for pretargeted imaging, we anticipate that the 18F-labeled d-TCO will find use in studies where increased hydrophilicity and fast bioconjugation are required.

Puzzling out graphic codes.

This response takes advantage of the diverse and wide-ranging series of commentaries to clarify some aspects of the target article, and flesh out other aspects. My central point is a plea to take graphic codes seriously as codes, rather than as a kind of visual art or as a byproduct of spoken language; only in this way can the puzzle of ideography be identified and solved. In this perspective, I argue that graphic codes do not derive their expressive power from iconicity alone (unlike visual arts), and I clarify the peculiar relationship that ties writing to spoken language. I then discuss three possible solutions to the puzzle of ideography. I argue that a learning account still cannot explain why ideographies fail to evolve, even if we emancipate the learning account from the version that Liberman put forward; I develop my preferred solution, the "standardization account," and contrast it with a third solution suggested by some commentaries, which says that ideographies do not evolve because they would make communication too costly. I consider, by way of conclusion, the consequences of these views for the future evolution of ideography.

Mechanochemical Solvent-Free One-Pot Synthesis of Poly-Functionalized 5-(Arylselanyl)-1H-1,2,3-triazoles Through a Copper(I)-Catalyzed Click Reaction.

A mechanochemistry-driven practical and efficient synthetic protocol for accessing diverse series of biologically relevant poly-functionalized 5-(arylselanyl)-1H-1,2,3-triazoles through copper(I)-catalyzed click reaction between aryl/heteroaryl acetylenes, diaryl diselenides, benzyl bromides, and sodium azide has been accomplished under high-speed ball-milling. Advantages of this method include operational simplicity, avoidance of using solvent and external heating, one-pot synthesis, short reaction time in minutes, good to excellent yields, broad substrate scope, and gram-scale applications. Furthermore, synthesized organoselenium compounds were synthetically diversified to biologically promising selenones.

Three Oxidative Addition Routes of Alkali Metal Aluminyls to Dihydridoaluminates and Reactivity with CO2.

Three distinct routes are reported to the soluble, dihydridoaluminate compounds, AM[Al(NONDipp )(H)2 ] (AM=Li, Na, K, Rb, Cs; [NONDipp ]2- =[O(SiMe2 NDipp)2 ]2- ; Dipp=2,6-iPr2 C6 H3 ) starting from the alkali metal aluminyls, AM[Al(NONDipp )]. Direct H2 hydrogenation of the heavier analogues (AM=Rb, Cs) produced the first examples of structurally characterized rubidium and caesium dihydridoaluminates, although harsh conditions were required for complete conversion. Using 1,4-cyclohexadiene (1,4-CHD) as an alternative hydrogen source in transfer hydrogenation reactions provided a lower energy pathway to the full series of products for AM=Li-Cs. A further moderation in conditions was noted for the thermal decomposition of the (silyl)(hydrido)aluminates, AM[Al(NONDipp )(H)(SiH2 Ph)]. Probing the reaction of Cs[Al(NONDipp )] with 1,4-CHD provided access to a novel inverse sandwich complex, [{Cs(Et2 O)}2 {Al(NONDipp )(H)}2 (C6 H6 )], containing the 1,4-dialuminated [C6 H6 ]2- dianion and representing the first time that an intermediate in the commonly utilized oxidation process of 1,4-CHD to benzene has been trapped. The synthetic utility of the newly installed Al-H bonds has been demonstrated by their ability to reduce CO2 under mild conditions to form the bis-formate AM[Al(NONDipp )(O2 CH)2 ] compounds, which exhibit a diverse series of eyecatching bimetallacyclic structures.

Emerging habitats: Design as a Worldmaking Agent

This special issue of Temes de Disseny explores emerging habitats and design as a worldmaking agent across a variety of scales, settings and disciplines. 
 Can design leverage its worldmaking potential using its symbolic and operative apparatuses to proliferate various versions of the world? Can design maintain and even multiply plural worlds in the face of global homogenisation? Contributing authors manifest these fundamental and intertwined questions through a rich and diverse series of contributions compiled from radical design practices, applied research, case studies, practice-based reflections and pictorials that are far-reaching in their intersectionality and forward orientation.

Synthetic approach to spiropyranocoumarins and their oxime derivatives

This study explores the synthesis of a diverse series of linear (spiro)pyranocoumarins and their corresponding oximes, compounds known for their promising biological activities. Building on previous work, the authors expand the array of target compounds, adding structural features such as dimethyl groups and various cycloaliphatic rings. The novel synthetic procedure applied herein couples o-hydroxyacetyl coumarins with respective ketones via Kabbe condensation, yielding 16 derivatives, including 12 new compounds. A further step engages these (spiro)pyranocoumarins with hydroxylamine hydrochloride, leading to oximes, selectively at the exocyclic oxygen atom of the chroman-4-one fragment. Optimizing synthesis conditions has increased product yields and reduced reaction times. Acidic hydrolysis of select compounds introduces additional carbonyl groups and facilitates deprotection, while the subsequent reaction with hydroxylamine hydrochloride produces dual-oxime compounds. These findings contribute to the ongoing development of pyranocoumarin and oxime-based therapeutics, with potential applications in treating various diseases

Performance evaluation of the smartphone-based AI cough monitoring app - Hyfe Cough Tracker against solicited respiratory sounds

Background: Emerging technologies to remotely monitor patients’ cough show promise for various clinical applications. Currently available cough detection systems all represent a trade-off between convenience and performance. The accuracy of such technologies is highly contingent on the clinical settings in which they are intended to be used. Moreover, establishing gold standards to measure this accuracy is challenging. Objectives: We present the first performance evaluation study of the Hyfe Cough Tracker app, a passive cough monitoring smartphone application. We evaluate performance for cough detection using continuous audio recordings obtained within a controlled environment and cough counting by trained individuals as the gold standard. We propose standard procedures to use multi-observer cough sound annotation from continuous audio recordings as the gold standard for evaluating automated cough detection devices. Methods: This study was embedded in a larger digital acoustic surveillance study (clinicaltrial.gov NCT04762693). Forty-nine participants were included and instructed to produce a diverse series of solicited sounds in 10-minute sessions. Simultaneously, continuous audio recording was performed using a MP3 recorder and two smartphones running Hyfe Cough Tracker app monitored and identified cough events. All continuous audio recordings were independently labeled by three medically-trained researchers. Results: Hyfe Cough Tracker app showed sensitivity of 91% and specificity of 98% with a very high correlation between the cough rate measured by Hyfe and that of human annotators (Pearson correlation of 0.968). A standardized approach to establish an acoustic gold standard for identifying cough sounds with multiple observers is presented. Conclusion: This is the first performance evaluation of a new smartphone-based cough monitoring system. Hyfe Cough Tracker can detect, record and count coughs from solicited cough-like explosive sounds in controlled acoustic environments with very high accuracy. Additional steps are required to validate the system in clinical and community settings.